For millimeter-wave networks, this paper presents a paradigm shift for leveraging time-consecutive camera images in handover decision problems. While making handover decisions, it is important to predict future long-term performance—e.g., the cumulative sum of time-varying data rates—proactively to avoid making myopic decisions. However, this study experimentally notices that a time-variation in the received powers is not necessarily informative for proactively predicting the rapid degradation of data rates caused by moving obstacles. To overcome this challenge, this study proposes a proactive framework wherein handover timings are optimized while obstacle-caused data rate degradations are predicted before the degradations occur. The key idea is to expand a state space to involve time-consecutive camera images, which comprises informative features for predicting such data rate degradations. To overcome the difficulty in handling the large dimensionality of the expanded state space, we use a deep reinforcement learning for deciding the handover timings. The evaluations performed based on the experimentally obtained camera images and received powers demonstrate that the expanded state space facilitates (i) the prediction of obstacle-caused data rate degradations from 500 ms before the degradations occur and (ii) superior performance to a handover framework without the state space expansion.

中文翻译：

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使用相机图像和深度强化学习进行主动性能预测的毫米波网络切换管理

对于毫米波网络，本文提出了在切换决策问题中利用时间连续相机图像的范式转变。在做出切换决策时，重要的是主动预测未来的长期性能——例如，随时间变化的数据速率的累积总和——以避免做出短视的决策。然而，这项研究在实验上注意到，接收功率的时间变化不一定能提供信息来主动预测由移动障碍物引起的数据速率的快速下降。为了克服这一挑战，本研究提出了一个主动框架，其中优化了切换时间，同时在退化发生之前预测了障碍引起的数据速率退化。关键思想是扩展状态空间以包含时间连续的相机图像，其中包含用于预测此类数据速率降级的信息特征。为了克服处理扩展状态空间的大维度的困难，我们使用深度强化学习来决定切换时间。基于实验获得的相机图像和接收功率进行的评估表明，扩展的状态空间有助于 (i) 预测由障碍物引起的数据速率从退化发生前 500 毫秒开始下降，以及 (ii) 优于无切换框架的性能状态空间扩展。